Vector Databases and Embeddings for Practical Applications

As artificial intelligence and large language models reshape software development, traditional relational databases are no longer enough to handle unstructured data. To build intelligent search, recommendation engines, and retrieval-augmented generation systems, you must understand how to store and query high-dimensional vector representations. This written course guides you from the fundamental mathematics of embeddings to deploying robust database solutions. You will transition from a traditional developer to an engineer capable of designing and implementing semantic search systems that understand context, meaning, and relationships in data. What you'll learn: - Understand the core concepts of vector embeddings and high-dimensional spaces - Compare key vector database architectures and indexing algorithms like HNSW and IVF - Implement semantic search, hybrid search, and multilingual search patterns - Configure metadata filtering to refine and restrict query results efficiently - Apply vector databases to retrieval-augmented generation architectures - Manage database performance, scaling strategies, and distance metrics Starting with foundational definitions of vector spaces and similarity metrics, this text-only course guides you step-by-step through practical indexing strategies, hands-on query formulation, and architectural patterns for real-world applications. Each concept is reinforced with clear explanations and structured code snippets. This course is designed for software developers, data enthusiasts, and engineers who are new to vector databases and want to build a solid, practical understanding of semantic data retrieval. No prior experience with vector databases or advanced machine learning is required.